{"id":"https://openalex.org/W2788907956","doi":"https://doi.org/10.1109/ijcnn.2018.8489629","title":"A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector","display_name":"A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2788907956","doi":"https://doi.org/10.1109/ijcnn.2018.8489629","mag":"2788907956"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1802.09567","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042365972","display_name":"Rayson Laroca","orcid":"https://orcid.org/0000-0003-1943-2711"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Rayson Laroca","raw_affiliation_strings":["Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091502361","display_name":"Evair Borges Severo","orcid":null},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Evair Severo","raw_affiliation_strings":["Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026966525","display_name":"Luiz A. Zanlorensi","orcid":"https://orcid.org/0000-0003-2545-0588"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz A. Zanlorensi","raw_affiliation_strings":["Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038884704","display_name":"Luiz S. Oliveira","orcid":"https://orcid.org/0000-0002-0595-5370"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz S. Oliveira","raw_affiliation_strings":["Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics, Federal University of Paran&#x00E1; (UFPR), Curitiba, PR, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054081700","display_name":"Gabriel Resende Gon\u00e7alves","orcid":"https://orcid.org/0000-0001-9133-0221"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gabriel Resende Goncalves","raw_affiliation_strings":["Department of Computer Science, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044741265","display_name":"William Robson Schwartz","orcid":"https://orcid.org/0000-0003-1449-8834"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"William Robson Schwartz","raw_affiliation_strings":["Department of Computer Science, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil","institution_ids":["https://openalex.org/I110200422"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033489756","display_name":"David Menotti","orcid":"https://orcid.org/0000-0003-2430-2030"},"institutions":[{"id":"https://openalex.org/I110200422","display_name":"Universidade Federal de Minas Gerais","ror":"https://ror.org/0176yjw32","country_code":"BR","type":"education","lineage":["https://openalex.org/I110200422"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"David Menotti","raw_affiliation_strings":["Department of Computer Science, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Minas Gerais (UFMG), Belo Horizonte, MG, Brazil","institution_ids":["https://openalex.org/I110200422"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5042365972"],"corresponding_institution_ids":["https://openalex.org/I52418104"],"apc_list":null,"apc_paid":null,"fwci":46.425,"has_fulltext":false,"cited_by_count":577,"citation_normalized_percentile":{"value":0.99880444,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9772999882698059,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9742000102996826,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7808229923248291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7705819606781006},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6824995279312134},{"id":"https://openalex.org/keywords/license","display_name":"License","score":0.641819953918457},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5703028440475464},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5493388772010803},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5058688521385193},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4963884949684143},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4923800230026245},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.43885117769241333},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4373871982097626},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42014530301094055},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.37699759006500244}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7808229923248291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7705819606781006},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6824995279312134},{"id":"https://openalex.org/C2780560020","wikidata":"https://www.wikidata.org/wiki/Q79719","display_name":"License","level":2,"score":0.641819953918457},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5703028440475464},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5493388772010803},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5058688521385193},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4963884949684143},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4923800230026245},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.43885117769241333},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4373871982097626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42014530301094055},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.37699759006500244},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489629","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489629","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1802.09567","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.09567","pdf_url":"https://arxiv.org/pdf/1802.09567","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1802.09567","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.09567","pdf_url":"https://arxiv.org/pdf/1802.09567","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.46000000834465027,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1498947572","https://openalex.org/W1958236864","https://openalex.org/W1981438058","https://openalex.org/W2005460505","https://openalex.org/W2024121930","https://openalex.org/W2095565388","https://openalex.org/W2102608210","https://openalex.org/W2108598243","https://openalex.org/W2135449683","https://openalex.org/W2273196628","https://openalex.org/W2279655419","https://openalex.org/W2280335824","https://openalex.org/W2309015593","https://openalex.org/W2508224586","https://openalex.org/W2550229148","https://openalex.org/W2562941417","https://openalex.org/W2570343428","https://openalex.org/W2575354312","https://openalex.org/W2604593829","https://openalex.org/W2733507598","https://openalex.org/W2759538494","https://openalex.org/W2765763274","https://openalex.org/W2767598133","https://openalex.org/W2962778460","https://openalex.org/W2962829835","https://openalex.org/W2963037989","https://openalex.org/W2963087201","https://openalex.org/W3011698205","https://openalex.org/W3103158247","https://openalex.org/W4294391685","https://openalex.org/W6695114890","https://openalex.org/W6735570655","https://openalex.org/W6740942534","https://openalex.org/W6744862879","https://openalex.org/W6745085275","https://openalex.org/W6745829397"],"related_works":["https://openalex.org/W2606446052","https://openalex.org/W2036021480","https://openalex.org/W3195777957","https://openalex.org/W2382668227","https://openalex.org/W2348482143","https://openalex.org/W2024584030","https://openalex.org/W4312856422","https://openalex.org/W2969228573","https://openalex.org/W4387272257","https://openalex.org/W4310880131"],"abstract_inverted_index":{"Automatic":[0],"License":[1,98],"Plate":[2],"Recognition":[3],"(ALPR)":[4],"has":[5],"been":[6],"a":[7,38,87,130,162,168],"frequent":[8],"topic":[9],"of":[10,19,120,133,200,214,233],"research":[11,245],"due":[12],"to":[13,177,243,250],"many":[14,18,33],"practical":[15],"applications.":[16],"However,":[17],"the":[20,46,116,211,223,244],"current":[21],"solutions":[22],"are":[23,56,67,193],"still":[24],"not":[25],"robust":[26,39,68],"in":[27,74,111,115],"real-world":[28],"situations,":[29],"commonly":[30],"depending":[31],"on":[32,45],"constraints.":[34],"This":[35,179],"paper":[36],"presents":[37],"and":[40,58,77,83,101,135,146,151,154,184,191,195,205,235],"efficient":[41],"ALPR":[42,62,106],"system":[43,128,227],"based":[44],"state-of-the-art":[47],"YOLO":[48],"object":[49],"detector.":[50],"The":[51,104],"Convolutional":[52],"Neural":[53],"Networks":[54],"(CNNs)":[55],"trained":[57],"finetuned":[59],"for":[60,80],"each":[61],"stage":[63],"so":[64],"that":[65],"they":[66],"under":[69],"different":[70,198],"conditions":[71],"(e.g.,":[72],"variations":[73],"camera,":[75],"lighting,":[76],"background).":[78],"Specially":[79],"character":[81],"segmentation":[82],"recognition,":[84],"we":[85,166],"design":[86],"two-stage":[88],"approach":[89,107],"employing":[90],"simple":[91],"data":[92],"augmentation":[93],"tricks":[94],"such":[95],"as":[96],"inverted":[97],"Plates":[99],"(LPs)":[100],"flipped":[102],"characters.":[103],"resulting":[105],"achieved":[108,129,217],"impressive":[109],"results":[110,158],"two":[112],"datasets.":[113],"First,":[114],"SSIG":[117],"dataset,":[118,175,210],"composed":[119],"2,000":[121],"frames":[122,186],"from":[123],"101":[124],"vehicle":[125],"videos,":[126],"our":[127,208,226],"recognition":[131,218,231],"rate":[132,232],"93.53%":[134],"47":[136],"Frames":[137],"Per":[138],"Second":[139],"(FPS),":[140],"performing":[141],"better":[142],"than":[143],"both":[144,189],"Sighthound":[145],"OpenALPR":[147],"commercial":[148,215],"systems":[149,216],"(89.80%":[150],"93.03%,":[152],"respectively)":[153],"considerably":[155],"outperforming":[156],"previous":[157],"(81.80%).":[159],"Second,":[160],"targeting":[161],"more":[163],"realistic":[164],"scenario,":[165],"introduce":[167],"larger":[169],"public":[170],"dataset":[171,180,239],"<sup":[172],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[173],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[174],"designed":[176],"ALPR.":[178],"contains":[181,197],"150":[182],"videos":[183],"4,500":[185],"captured":[187],"when":[188],"camera":[190],"vehicles":[192,201],"moving":[194],"also":[196],"types":[199],"(cars,":[202],"motorcycles,":[203],"buses":[204],"trucks).":[206],"In":[207],"proposed":[209],"trial":[212],"versions":[213],"rates":[219],"below":[220],"70%.":[221],"On":[222],"other":[224],"hand,":[225],"performed":[228],"better,":[229],"with":[230],"78.33%":[234],"35":[236],"FPS.The":[237],"UFPR-ALPR":[238],"is":[240],"publicly":[241],"available":[242],"community":[246],"at":[247],"https://web.inf.ufpr.br/vri/databases/ufpr-alpr/":[248],"subject":[249],"privacy":[251],"restrictions.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":72},{"year":2024,"cited_by_count":76},{"year":2023,"cited_by_count":94},{"year":2022,"cited_by_count":107},{"year":2021,"cited_by_count":95},{"year":2020,"cited_by_count":65},{"year":2019,"cited_by_count":50},{"year":2018,"cited_by_count":12}],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2018-03-06T00:00:00"}
